Generative Model-Enhanced Human Motion Prediction
This addresses robustness challenges in human motion prediction for applications like robotics and animation, though it is incremental as it builds on existing discriminative models.
The paper tackles the problem of predicting human motion under out-of-distribution (OoD) conditions by introducing a hybrid framework that augments discriminative models with a generative model, showing improved OoD robustness without compromising in-distribution performance.
The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here we formulate a new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and introduce a hybrid framework for hardening discriminative architectures to OoD failure by augmenting them with a generative model. When applied to current state-of-the-art discriminative models, we show that the proposed approach improves OoD robustness without sacrificing in-distribution performance, and can theoretically facilitate model interpretability. We suggest human motion predictors ought to be constructed with OoD challenges in mind, and provide an extensible general framework for hardening diverse discriminative architectures to extreme distributional shift. The code is available at https://github.com/bouracha/OoDMotion.